Multimodal behavior analysis in computer-enabled laboratories using nonverbal cues
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ORIGINAL PAPER
Multimodal behavior analysis in computer-enabled laboratories using nonverbal cues Sayani Banerjee1 · T. S. Ashwin1
· Ram Mohana Reddy Guddeti1
Received: 12 July 2019 / Revised: 12 March 2020 / Accepted: 28 April 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract In the modern era, there is a growing need for surveillance to ensure the safety and security of the people. Real-time object detection is crucial for many applications such as traffic monitoring, security, search and rescue, vehicle counting, and classroom monitoring. Computer-enabled laboratories are generally equipped with video surveillance cameras in the smart campus. But, from the existing literature, it is observed that the use of video surveillance data obtained from smart campus for any unobtrusive behavioral analysis is seldom performed. Though there are several works on the students’ and teachers’ behavior recognition from devices such as Kinect and handy cameras, there exists no such work which extracts the video surveillance data and predicts the behavioral patterns of both the students and the teachers in real time. Hence, in this study, we unobtrusively analyze the students’ and teachers’ behavioral patterns inside a teaching laboratory (which is considered as an indoor scenario of a smart campus). Here, we propose a deep convolution network architecture to classify and recognize an object in the indoor scenario, i.e., the teaching laboratory environment of the smart campus with modified Single-Shot MultiBox Detector approach. We used six different class labels for predicting the behavioral patterns of both the students and the teachers. We created our dataset with six different class labels for training deep learning architecture. The performance evaluation demonstrates that the proposed method performs better with an accuracy of 0.765 for classification and localization. Keywords Behavioural patterns · Nonverbal cues · Object detection · SSD
1 Introduction The technical advancements of the modern era have enabled the use of video surveillance data in most of the computer vision-based Internet of Things (IoT) applications [1]. Nowadays, one can find a surveillance camera in every corner of a smart city application [2]. With the rapid growth in usage of S. Banerjee and T. S. Ashwin have contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11760-020-01705-4) contains supplementary material, which is available to authorized users.
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T. S. Ashwin [email protected] Sayani Banerjee [email protected] Ram Mohana Reddy Guddeti [email protected]
1
Department of Information Technology, National Institute of Technology Karnataka Surathkal, Mangalore, India
the closed-circuit television (CCTV) cameras, real-time analysis becomes possible for any automated monitoring activity as we have multiple CCTV cameras in many locations to monitor everything simultaneously in real time. From protecting banks from thieves t
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